12 KiB
Instrumenting Grafana
Guidance, conventions and best practices for instrumenting Grafana using logs, metrics and traces.
Logs
Logs are files that record events, warnings and errors as they occur within a software environment. Most logs include contextual information, such as the time an event occurred and which user or endpoint was associated with it.
Usage
Use the pkg/infra/log package to create a named structured logger. Example:
import (
"fmt"
"github.com/grafana/grafana/pkg/infra/log"
)
logger := log.New("my-logger")
logger.Debug("Debug msg")
logger.Info("Info msg")
logger.Warning("Warning msg")
logger.Error("Error msg", "error", fmt.Errorf("BOOM"))
Naming conventions
Name the logger using lowercase characters, e.g. log.New("my-logger") using snake_case or kebab-case styling.
Prefix the logger name with an area name when using different loggers across a feature or related packages, e.g. log.New("plugin.loader") and log.New("plugin.client").
Start the log message with a capital letter, e.g. logger.Info("Hello world") instead of logger.Info("hello world"). The log message should be an identifier for the log entry, avoid parameterization in favor of key-value pairs for additional data.
Prefer using camelCase style when naming log keys, e.g. remoteAddr, to be consistent with Go identifiers.
Use the key error when logging Go errors, e.g. logger.Error("Something failed", "error", fmt.Errorf("BOOM")).
Validate and sanitize input coming from user input
If log messages or key/value pairs originates from user input they should be validated and sanitized.
Be careful to not expose any sensitive information in log messages e.g. secrets, credentials etc. It's especially easy to do by mistake when including a struct as value.
Log levels
When to use which log level?
- Debug: Informational messages of high frequency and/or less-important messages during normal operations.
- Info: Informational messages of low frequency and/or important messages.
- Warning: Should in normal cases not be used/needed. If used should be actionable.
- Error: Error messages indicating some operation failed (with an error) and the program didn't have a way of handle the error.
Contextual logging
Use a contextual logger to include additional key/value pairs attached to context.Context, e.g. traceID, to allow correlating logs with traces and/or correlate logs with a common identifier.
You must Enable tracing in Grafana to get a traceID
Example:
import (
"context"
"fmt"
"github.com/grafana/grafana/pkg/infra/log"
)
var logger = log.New("my-logger")
func doSomething(ctx context.Context) {
ctxLogger := logger.FromContext(ctx)
ctxLogger.Debug("Debug msg")
ctxLogger.Info("Info msg")
ctxLogger.Warning("Warning msg")
ctxLogger.Error("Error msg", "error", fmt.Errorf("BOOM"))
}
Enable certain log levels for certain loggers
During development it's convenient to enable certain log level, e.g. debug, for certain loggers to minimize the generated log output and make it easier to find things. See [log.filters] for information how to configure this.
It's also possible to configure multiple loggers:
[log]
filters = rendering:debug \
; alerting.notifier:debug \
oauth.generic_oauth:debug \
; oauth.okta:debug \
; tsdb.postgres:debug \
; tsdb.mssql:debug \
; provisioning.plugins:debug \
; provisioning:debug \
; provisioning.dashboard:debug \
; provisioning.datasources:debug \
datasources:debug \
data-proxy-log:debug
Metrics
Metrics are quantifiable measurements that reflect the health and performance of applications or infrastructure.
Consider using metrics to provide real-time insight into the state of resources. If you want to know how responsive your application is or identify anomalies that could be early signs of a performance issue, metrics are a key source of visibility.
Metric types
See Prometheus metric types for a list and description of the different metric types you can use and when to use them.
There are many possible types of metrics that can be tracked. One popular method for defining metrics is the RED method.
Naming conventions
Use the namespace grafana as that would prefix any defined metric names with grafana_. This will make it clear for operators that any metric named grafana_* belongs to Grafana.
Use snakecase style when naming metrics, e.g. _http_request_duration_seconds instead of httpRequestDurationSeconds.
Use snakecase style when naming labels, e.g. _status_code instead of statusCode.
If metric type is a counter, name it with a _total suffix, e.g. http_requests_total.
If metric type is a histogram and you're measuring duration, name it with a _<unit> suffix, e.g. http_request_duration_seconds.
If metric type is a gauge, name it to denote it's a value that can increase and decrease , e.g. http_request_in_flight.
Label values and high cardinality
Be careful with what label values you add/accept. Using/allowing too many label values could result in high cardinality problems.
If label values originates from user input they should be validated. Use metricutil.SanitizeLabelName(<label value>) from pkg/infra/metrics/metricutil package to sanitize label names. Very important to only allow a pre-defined set of labels to minimize the risk of high cardinality problems.
Be careful to not expose any sensitive information in label values, e.g. secrets, credentials etc.
Guarantee the existence of metrics
If you want to guarantee the existence of metrics before any observations has happened there's a couple of helper methods available in the pkg/infra/metrics/metricutil package.
How to collect and visualize metrics locally
-
Ensure you have Docker installed and running on your machine
-
Start Prometheus
make devenv sources=prometheus -
Run Grafana, and create a Prometheus datasource if you do not have one yet. Set the server URL to
http://localhost:9090, enable basic auth, and type in the same auth you have for local Grafana -
Use Grafana Explore or dashboards to query any exported Grafana metrics. You can also view them at http://localhost:3000/metrics
Traces
A distributed trace is data that tracks an application request as it flows through the various parts of an application. The trace records how long it takes each application component to process the request and pass the result to the next component. Traces can also identify which parts of the application trigger an error.
Usage
Grafana currently supports two tracing implementations, OpenTelemetry and OpenTracing. OpenTracing is deprecated, but still supported until we remove it. The two different implementations implements the Tracer and Span interfaces, defined in the pkg/infra/tracing package, which you can use to create traces and spans. To get a hold of a Tracer you would need to get it injected as dependency into your service, see Services for more details.
Example:
import (
"fmt"
"github.com/grafana/grafana/pkg/infra/tracing"
"go.opentelemetry.io/otel/attribute"
)
type MyService struct {
tracer tracing.Tracer
}
func ProvideService(tracer tracing.Tracer) *MyService {
return &MyService{
tracer: tracer,
}
}
func (s *MyService) Hello(ctx context.Context, name string) (string, error) {
ctx, span := s.tracer.Start(ctx, "MyService.Hello")
// this make sure the span is marked as finished when this
// method ends to allow the span to be flushed and sent to
// storage backend.
defer span.End()
// Add some event to show Events usage
span.AddEvents(
[]string{"message"},
[]tracing.EventValue{
{Str: "checking name..."},
})
if name == "" {
err := fmt.Errorf("name cannot be empty")
// record err as an exception span event for this span
span.RecordError(err)
return "", err
}
// Add some other event to show Events usage
span.AddEvents(
[]string{"message"},
[]tracing.EventValue{
{Str: "name checked"},
})
// Add attribute to show Attributes usage
span.SetAttributes("my_service.name", name, attribute.Key("my_service.name").String(name))
return fmt.Sprintf("Hello %s", name), nil
}
Naming conventions
Span names should follow the guidelines from OpenTelemetry.
| Span Name | Guidance |
|---|---|
| get | Too general |
| get_account/42 | Too specific |
| get_account | Good, and account_id=42 would make a nice Span attribute |
| get_account/{accountId} | Also good (using the “HTTP route”) |
Span attribute and span event attributes should follow the Attribute naming specification from OpenTelemetry. Good attribute key examples:
- service.version
- http.status_code
See Trace semantic conventions from OpenTelemetry for additional conventions regarding well-known protocols and operations.
Span names and high cardinality
Be careful with what span names you add/accept. Using/allowing too many span names could result in high cardinality problems.
Validate and sanitize input coming from user input
If span names, attribute or event values originates from user input they should be validated and sanitized. It's very important to only allow a pre-defined set of span names to minimize the risk of high cardinality problems.
Be careful to not expose any sensitive information in span names, attribute or event values, e.g. secrets, credentials etc.
How to collect, visualize and query traces (and correlate logs with traces) locally
1. Start Jaeger
make devenv sources=jaeger
2. Enable tracing in Grafana
To enable tracing in Grafana, you must set the address in your config.ini file
opentelemetry tracing (recommended):
[tracing.opentelemetry.jaeger]
address = http://localhost:14268/api/traces
opentracing tracing (deprecated/not recommended):
[tracing.jaeger]
address = localhost:6831
3. Search/browse collected logs and traces in Grafana Explore
You need provisioned gdev-jaeger and gdev-loki datasources, see developer dashboard and data sources for setup instructions.
Open Grafana explore and select gdev-loki datasource and use the query {filename="/var/log/grafana/grafana.log"} | logfmt.
You can then inspect any log message that includes a traceID and from there click on gdev-jaeger to split view and inspect the trace in question.
4. Search/browse collected traces in Jaeger UI
You can open http://localhost:16686 to use the Jaeger UI for browsing and searching traces.